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Final Gleason score prediction using discriminant analysis and support vector machine based on preoperative multiparametric MR imaging of prostate cancer at 3T.

Citak-Er F, Vural M, Acar O, Esen T, Onay A, Ozturk-Isik E - Biomed Res Int (2014)

Bottom Line: Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively.Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively.SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics and Bioengineering, Yeditepe University, İnönü Mah., Kayışdağı Cad, 26 Ağustos Yerleşimi, Ataşehir, 34755 Istanbul, Turkey.

ABSTRACT

Objective: This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters.

Materials and methods: Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation.

Results: Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively.

Conclusion: SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.

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Related in: MedlinePlus

The results of data uncorrelation via Gaussian kernel PCA after each iteration of SVM-RFE. The scree plots of the eigenvalues versus principal components (PCs) (a) and the distribution of the first three principal components at each data point (b) are given. The high-grade data are labeled as circles, while the low-grade data are labeled as dots.
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fig2: The results of data uncorrelation via Gaussian kernel PCA after each iteration of SVM-RFE. The scree plots of the eigenvalues versus principal components (PCs) (a) and the distribution of the first three principal components at each data point (b) are given. The high-grade data are labeled as circles, while the low-grade data are labeled as dots.

Mentions: Then, a standard or a Gaussian kernel PCA was applied to uncorrelate the data after each iteration of the SVM-RFE. The sigma (σ) parameter of Gaussian kernel and the size of the remaining dimension were considered separately at each iteration. Figure 2 shows the results of data uncorrelation via Gaussian kernel PCA after each iteration of SVM-RFE. The scree plots of the eigenvalues versus the principal components with the corresponding cumulative percent variances and the first three principal components of the uncorrelated data are shown. The principal component located at the elbow of the scree plot was selected as the cut-off point for the total number of principal components. A cut-off point that keeps 80% to 90% of the data variance was selected [30].


Final Gleason score prediction using discriminant analysis and support vector machine based on preoperative multiparametric MR imaging of prostate cancer at 3T.

Citak-Er F, Vural M, Acar O, Esen T, Onay A, Ozturk-Isik E - Biomed Res Int (2014)

The results of data uncorrelation via Gaussian kernel PCA after each iteration of SVM-RFE. The scree plots of the eigenvalues versus principal components (PCs) (a) and the distribution of the first three principal components at each data point (b) are given. The high-grade data are labeled as circles, while the low-grade data are labeled as dots.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4269213&req=5

fig2: The results of data uncorrelation via Gaussian kernel PCA after each iteration of SVM-RFE. The scree plots of the eigenvalues versus principal components (PCs) (a) and the distribution of the first three principal components at each data point (b) are given. The high-grade data are labeled as circles, while the low-grade data are labeled as dots.
Mentions: Then, a standard or a Gaussian kernel PCA was applied to uncorrelate the data after each iteration of the SVM-RFE. The sigma (σ) parameter of Gaussian kernel and the size of the remaining dimension were considered separately at each iteration. Figure 2 shows the results of data uncorrelation via Gaussian kernel PCA after each iteration of SVM-RFE. The scree plots of the eigenvalues versus the principal components with the corresponding cumulative percent variances and the first three principal components of the uncorrelated data are shown. The principal component located at the elbow of the scree plot was selected as the cut-off point for the total number of principal components. A cut-off point that keeps 80% to 90% of the data variance was selected [30].

Bottom Line: Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively.Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively.SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics and Bioengineering, Yeditepe University, İnönü Mah., Kayışdağı Cad, 26 Ağustos Yerleşimi, Ataşehir, 34755 Istanbul, Turkey.

ABSTRACT

Objective: This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters.

Materials and methods: Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation.

Results: Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively.

Conclusion: SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.

Show MeSH
Related in: MedlinePlus